The chemical diversity possible amongst the suspected 10180 possible drug-like molecules is immense, and a given combinatorial library can only hope to capture a tiny fraction of this diversity space. Molecular library design strategies use chemoinformatic techniques to select a diverse set of molecules for library synthesis. The molecular selection process involves the calculation of the chemical characteristics of each member of the library, using hundreds of chemical descriptors. It is therefore possible to derive a “diverse” library, where the molecules differ from each other as much as possible in descriptor space, or a “focussed” library, where the molecules are similar in descriptor space to a known active. With hundreds of potential descriptors it is difficult to know which descriptors are important or essential for describing biological activity. These approaches consequently optimise libraries in the chemical universe but do not identify molecules that could modulate biological function.
It is becoming evident that the synthesis of large combinatorial libraries makes sense only if guided by sound library design principles. It is generally accepted that focussing libraries can lead to a 10-100 fold increase in the discovery of “hits” (i.e candidate or lead molecules).
A significant number of pharmaceutical targets involve the mimicking or inhibition of protein interactions with other molecules. With the rapid advance of the human genome project, it is likely that many more protein interaction targets will be identified.
Proteins are amino acid polymers that fold into a globular structure. This globular structure, in general, has a hydrophobic interior. The structure of proteins is defined by the polymeric nature of the backbone and includes secondary structure elements such as helices, sheets, loops and turns. Whilst the description of protein structure by the nature of its polymeric backbone (its “skeleton”) is useful for comparing one protein to another, it is not useful when describing the structural elements of various molecular recognition events of proteins. This is because molecular recognition is a surface phenomenon and proteins use large flat surface areas ranging from 1150 to 4660 Å2, comprising on average 211 atoms from 52 amino acid residues. These binding surfaces may be continuous (such as β-turns and loops), or discontinuous surfaces that comprise 1-11 segments (where a segment is separated by at least 5 amino acid residues and can be from a different secondary structure) with an average of 5 segments per interface.